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Development Of Wireless Sensor Network For Distribution System Management Reetu, Shabana Mehfuz, Member IEEE, Mini S. Thomas, Senior Member IEEE these sensors can work in harsh and tough environment Other advantages are safety, low cost, easy to operate, real time information, less operating time, less maintenance, small size , ease of use, wide area of applications[1,2].

The aim of the work is the applicability of wireless sensors and distributed local control in the management of electrical distribution networks. The general objective is to design architecture primarily for fault management applications. A new concept is developed for the fault management of distribution automation in this paper. The major technical reason for developing a new fault management concept instead of using the previous proposals is to enhance the integration of wireless sensors within power system equipment. The technical goal is to develop a simple short circuit and earth fault detection and location method using distributed wireless sensors that measure only phase current. Previously the directional relays and fault indicators were used for fault management [10] .

These methods were time consuming and chances of errors were more. Wireless sensors installed in the branches of remotely monitored switching stations or secondary substations, indicated the branch in which the fault has occurred [4, 5]. Combining this information with the data received from fault distance calculations at the substation makes fast and efficient fault isolation and power restoration to the healthy feeders and branches possible. The current measurements made by these sensors located into these feeders are shown to be useful in fault management. This is achieved by analyzing the trend of difference in measurement made before and during the fault. This paper has been further divided into the following sections. Section II describes the characteristics of wireless sensor. Section III gives the power system faults management concept with wireless sensor application. Network configuration and simulation is given in section IV .conclusion of paper is given in section 5.

Abstract-- This paper describes a new method for distribution system fault management with sensor applications. The concept is based on distributed wireless sensors that are attached to the incoming and out going power lines of the different feeders. The main novelty of the concept is in detecting and locating faults by combining the power distribution network characteristics on system level. The feasibility of the concept is shown with several simulations done in PSCAD environment.

Index terms—wireless sensors, power system management, distribution system, fault management.

I.

INTRODUCTION

In today’s operational and competitive electric utility marketplace reliable and continuous supply of power is needed. For this we require distribution automation. Distribution automation (DA) refers to the efficient management of modern electrical distribution systems so that customer demand for safe and reliable access to electricity is satisfied. .Distribution automation provides such facilities to consumer with high performance and satisfaction. DA comprises a set of functions and a set of information systems that form the toolbox for network operation and management from a distribution system operation perspective, wireless sensors give an opportunity to safely and cost efficiently increase measurement coverage of the network, including locations where wiring is impossible. Hence, more extensive and accurate real-time information regarding the state of the system becomes available to the operator.

This means that the components and the network can safely be run closer to their technical limits and that vital information for condition based maintenance of the network assets can be elicited. Sensor networks are the key to gathering the information needed by smart environments, whether in buildings, utilities, industrial, home, shipboard, transportation systems automation, or elsewhere.Recent terrorist and guerilla warfare countermeasures require distributed networks of sensors that can be deployed using, e.g. aircraft, and have selforganizing capabilities. In such applications, running wires or cabling is usually impractical. A sensor network is required that is fast and easy to install and maintain as

II.WIRELESS SENSOR CHARACTERISTICS A wireless sensor is a small device which is generally composed of four main components: Power supply, Radio communication, interfaces Microcontroller, and Sensor interface .fig 1 shows all these components.

Reetu is with Department of electrical Engineerin,Delhi Technological University ,Delhi, INDIA (e-mail: reetu.jmi@gmail.com).

Shabana Mehfuz is with Department of Electrical Engineering Jamia Millia Islamia, New Delhi, INDIA (e-mail: Mehfuz_shabana@yahoo.com).

Mini.S.Thomas is with Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, INDIA (e-mail: mini@ieee.org).

263 Š 2009 ACEEE

Proc. of Int. Conf. on Control, Communication and Power Engineering 2010

Sensing interface is used for filtering and data fusing.

Although no generic approach can be given, it is clear that fewer samples and shorter duty cycles significantly reduce the average energy consumption. An important design property, therefore, is to minimize the information needed from sensors, use them only to extract the most important process data, and replace the missing system information with intelligence and functionality on a higher system level.

Fig 1: Illustration of sensor components

A wireless sensor is self-powered. This means that it uses either a battery or energy extracted from the environment. A battery is used in many prototype implementations but it is neither a feasible nor a cost efficient solution in the highly distributed or embedded applications where the sensors eventually will be used.

Possible energy sources are therefore sunlight, vibrations, wind, heat and magnetic fields. Additional energy saving is attained by shutting down the sensor whenever no events occur. Hence, the radio device and the microcontroller are put into sleep mode when a task has been executed and are woken up again the next time an event occurs. The power consumption of a radio is determined by the modulation scheme, data rate and transmit power level .To minimize the power dissipation of the radio component, it is kept in sleep mode (or shut down) between communications. This means that the sensor is mostly unreachable and to communicate, it must either agree on a communication schedule with its neighbors, or the system must accept that only sensors initiates communication [7, 8].

Processing unit composed of microcontrollers, analog to digital converters and memory. Processing unit typically operating on a license-free band. Sampling, analogue to digital conversion, filtering and processing draw energy. When developing wireless sensors, a tradeoff should be made between the processing done in the sensor and the data transmitted to the receiving station for further processing. Preprocessing generally reduces the amount of data transmitted and thus communication energy dissipation, Wireless sensors are used to better manage service downtime, which can be significantly cut with wireless fault finding applications [4]

Fig 3: Wireless sensor network architecture

The sensor network architecture is based upon the sensor nodes and the link with control centre. There is one sink node which is intermediate between the deployed field and task manager. Wireless sensor for real-time Network (WSN) technology has created new communication paradigms and reliable monitoring requirement of the electric systems. Sensors have higher fault tolerance, efficient data processing, self configuration and organization features and improved accuracy [3].The sink and task manager (control centre) communicates via satellites and internet.

It is very important to understand how this sensor behaves with respect to the environment in case of fault management of distribution system. It works in the following manner; (1)-A sensor has only one simple triggering level. This triggering level is activated when the sensor detects that the periodically measured current is zero due to fault.

(2)-When this occurs, the sensor assumes that the circuit breaker (CB) has been opened as the consequence of a fault.

(3)-As a response to this event, the sensor sends the contents of a data buffer to a nearby data concentrator by the processing unit.

(4)-The buffer contains the periodically measured current values, thus also the current before and during the fault.

These values are used to calculate the fault location in the network control center.

III. WIRELESS SENSOR CONCEPT FOR FAULT MANAGEMENT The most common fault type in an electrical distribution network with an unearthed neutral is a singlephase-to-earth fault. A short circuit in a network branch is assumed if the phase current reaches a magnitude above a threshold and stays above that threshold level for at least certain duration in time. Short circuit faults are relatively easy to be detected and located in radial networks

Fig 2: Wireless sensor network

264 Š 2009 ACEEE

Proc. of Int. Conf. on Control, Communication and Power Engineering 2010

because the fault current is almost always significantly higher than the load current. Earth faults, on the other hand, are more problematic because the fault current component is small and depends on several factors in the environment. Generally, the properties of managing earth faults depend on the network topology, fault location, fault resistance and the type of earthling being used as shown in reference [5, 6]. In networks with an ungrounded neutral, the earth fault current depends mostly on the currents flowing through the earth capacitances of the second phases and on the fault resistance as shown in fig 4.

Fig 5 shows how the zones are divided [4, 5]. A zone is considered to cover a network segment between two secondary substations or more generally between two branches (i.e., no load tapping is allowed inside a zone) the network is divided into zones where the ends of a zone are defined by the location of a set of sensors. Each zone has two sensors which are placed at the starting and end of zone. Faults are located according to this new method used with the sensors as shown in fig 6. Consider the network with an ungrounded neutral in the network.

Sensors are the black boxes at locations A, B, and C. For each line three sensors are placed almost equidistant from each other. The dotted lines denote the direction of the fault currents. The grey areas show the distribution of the fault current amplitude that originates from the evenly distributed earth capacitances of phase wires [4].the capacitors are placed at the end of the zones with earth grounded.

Fig4: Unearthed distribution n/w with fault

The older methods of fault detection use relays.

The fault is detected by a directional protective relay in the substation, which trips if the zero-sequence current, the neutral voltage, and the phase shift between these violate the configured settings [10, 11] .The fault location is determined by splitting the feeder into sections and by testing in which section the fault occurs. There are other techniques which can be used for determining the fault section like neural network and genetic algorithm [9, 12].To minimize the fault-management time, remotely readable fault indicators can be installed. With these, the faulty branch can be directly located and the correct switching can be accomplished. The major disadvantage of these indicators is long tine and chances of error in the detection of fault place. The technical requirements for developing a fault management concept for distribution system in this project are short circuit faults shall be detected and located using only current measurements.

This implies that the zero sequence current, the neutral voltage and the phase angle between these will not be available for the fault management applications.

Fig 6: location of sensor and fault current path

The steps for fault calculation are as follows: (1) Measurement of fault current: fault current at each sensor location is first determined. This is achieved by calculating the vector difference of the current measured before the fault and the current measured during the fault, (2) Measurement of phase shift: The current magnitude before and during the fault and the phase shift between these is measured.

(3) Calculation of vector difference at end of zones: the vector differences at subsequent sensor locations are subtracted from each other.

Fig 5: Distribution of zones in system

265 Š 2009 ACEEE

Proc. of Int. Conf. on Control, Communication and Power Engineering 2010 Graphs

IV. NETWORK SIMULATION USING PSCAD SOFTWARE

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The network is simulated using PSCAD software (fig 7) and then fault is crated at different locations in different branches. The concept of fault management is applied and fault current component is calculated. This component is proportional to the location of a sensor and the length of the lines in the part of the network that is considered by the fault (i.e., all network segments of feeders connected to the substation and the line length between the substation and the fault of the faulty feeder).

Because the earth fault occurs in one network feeder, it is reasonable to assume that the total length of the lines in the sections limited by a pair of sensor sets is substantially shorter than the total length of the network considered. The essential factor here is that the fault circuit is closed through the earth capacitance and, hence, the fault current is spread along all lines, with the exception of the fault location where 100% of the fault current flows into the ground.

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Fig 9: Current waveform after the fault

Fig 8 and 9 shows the magnitude of current at different conditions. These graphs are showing the current which is used for determination of fault position. After that the algorithm is applied step by step. Calculation is done for the fault current component which gives the location of fault in network. The fault current component that originates from the earth capacitances of the phase lines.

This fault current component is proportional to the location of a sensor and the length of the lines in the part of the network that is considered by the fault.

V . CONCLUSION

Fig 7: simulated network

The current waveforms before and after the fault are drawn. The magnitude of fault current is higher than the current under normal current. The fault location is determined using the data received from these waveforms.

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REFERENCES [1] G.Ferrari, P.Medagliani, S.Di Piazza, ”Wireless sensor network: performance analysis in indoor scenarios” EUROSIP journalVOL.2007,article ID 81864 ,January 2007.

[2] Carlos F.Garcia-Hernandez, Pablo H.Gonzalez, Jesus.A parez ”Wireless sensor network and applications” International journal of network Security,VOL 7 no.3. March 2007 [3]V.C.Gungor,F.C.lambert,”A Survey on Communication networks for electric system automation” ELSEVIER journal of Computer Networks ,July 2007 [4] Mikael M.Nordman and Taneli Korhonen,”Design of a concept and wireless sensor for locating earth faults in unearthed electrical distribution networks”, IEEE transactions, on power delivery,vol.21.no.3 p.p1074-1082 .July 2006.

[5] M. Nordman and M. Lehtonen, “A Wireless sensor concept for managing electrical distribution networks,” presented at the IEEE Power Eng. Soc. Power Systems Conf. Expo., New York, Oct. 10–13, 2004.

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It is seen that the architecture was developed supporting the use of wireless sensors and distributed functions in the management of electrical distribution networks. The concept is based on detecting a fault when the circuit breaker in the primary substation is opened due to a fault. Faults are located by comparing the current measured before and during a fault at different locations in the network. The simulation is done successfully in PSCAD and waveforms generated are presented.

Calculation of fault current component and point of fault is detected in the network using this concept.

Proc. of Int. Conf. on Control, Communication and Power Engineering 2010 Reetu received her Master degree in Electrical Power System Management from Jamia Millia Islamia, New Delhi, Persuing Ph.D from jamia Millia Islamia., presently working with Departmenent of Electrical Engineering,, DTU. Her research interests are Wireless sensor networks and Distribution System Management Shabana Mehfuz received her B.Tech degree in electrical engineering from Jamia Millia Islamia, India in 1996, M.Tech degree in Computer technology from IIT Delhi in 2003and Ph.d from Department of Computer Engineering at Jamia Millia Islamia in 2008. She is currently working as a Reader at the Department of Electrical Engineering Jamia Millia Islamia. Her research interest includes artificial intelligence and QoS issues in Mobile Ad Hoc Networks.

Mini S. Thomas (M-88, SM-99), graduated from University of Kerala in 1984 completed her M.Tech from IIT Madras in 1986 (both with gold medals) & PhD from IIT Delhi in 1991, all in Electrical Engineering. Her employment experiences include Regional College, Calicut, Kerala, Delhi College of Engineering, New Delhi and presently as Professor in the Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi. Mini S. Thomas received the prestigious ‘career Award’ for young teacher, instituted by AICTE, Govt. of India, for the year 1999. She has published over 30 papers in International/National Journals & Conferences. Her current research interests are in SCADA/EMS system and intelligent protection of power system.

[6] M.Nordman, “An Architecture for wireless Sensor in Distributed Management of electrical distribution systems,” Ph.D. dissertation, power system lab.

Helsinki univ.technol.Espoo, Finland, 2004.

[7] M. Vieira, C. Coelho, D. da Silva, and J. Mata, “Survey on wireless sensor network devices,” presented at the Proc. IEEE Conf. Emerging Technologies and Factory Automation, Lisbon, Portugal, Sep. 16–19, 2003 [8] M. Nordman, M. Lehtonen, and O. Vähämäki, “Intelligent wireless sensors as fault and capacity monitors in power systems,” in Proc. 5: th Nordic Distribution Automation Conference, Nov. 7-8, 2002.

[9]. P. Bedekar, S. R. Bhide, and V. S. Kale,“Fault Section Estimation using Neural Network and Genetic Algorithm,” Proceedings of the International Conference on Emerging & Futuristic System and Technology (ICE-FST ’09), 9-11 April 2009, pp. 484-490.

[10] “Advance Power System Analysis and Dynamics” by L.P Singh (Third edition) New Age International Publishers.

[11]. Yong-Sik Choi ; Young-Jun Jeon ; “A study on sensor nodes attestation protocol in a Wireless Sensor Network” 12th International Conference on Advanced Communication Technology (ICACT), 2010 [12]. A multi-sink swarm-based routing protocol for Wireless Sensor Networks. IEEE Symposium on Computers and Communications, 2009. ISCC 2009.

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